毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar

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原始笔记链接:https://mp.weixin.qq.com/s?__biz=Mzg4MjgxMjgyMg==&mid=2247486680&idx=1&sn=edf41d4f95395d7294bc958ea68d3a68&chksm=cf51be21f826373790bc6d79bcea6eb2cb3d09bb1860bba0af0fd5e60c448ca006976503e460#rd
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IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar

毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar

毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar,# 论文阅读,论文阅读,笔记,雷达成像,压缩感知

Abstract

  • 背景

    • mmWave radars suffer from low angular resolution due to small apertures and conventional signal processing
    • 稀疏阵列雷达 can increase aperture size while minimizing power consumption and readout bandwidth
  • 方法 :提出 Compressive Implicit Radar (CoIR)

    • 目标: high accuracy sparse radar imaging using a single radar chip

    • Leverages : CNN decoder and compressed sensing

    • 贡献:

      设计稀疏线阵: with 5.5x fewer antennas than conventional MIMO arrays

      提出ComDecoder :a fully convolutional implicit neural network architecture

      证明了CoIR的有效性 :in both simulation and real-world experiments,且 不需要 auxiliary sensors

  • 实验结果

    • improved performance over standard mmWave radars and other untrained methods on simulated and real data
    • System does not require training data or auxiliary sensors
      毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar,# 论文阅读,论文阅读,笔记,雷达成像,压缩感知

1 INTRODUCTION

基于光学的Depth imaging及其缺点

  • Depth imaging
    • crucial for applications like SLAM, autonomous driving, security monitoring
  • Typical sensors: cameras, LiDAR
    • Cameras: high-resolution near-field depth imaging
    • LiDAR: directly outputs dense point cloud with high range/angular resolution
  • Limitation : degraded performance in visually degraded environments like fog, smoke

毫米波雷达成像的优点和挑战

  • 优点
    • penetrate through fog/smoke without performance degradation
  • 挑战
    • low angular resolution δ ≈ λ / d \delta ≈ \lambda/d δλ/d
    • Increasing d d d increases cost, power consumption and readout bandwidth

已有提高角分辨率的工作和缺陷

  • 已有思路
    • Large physical arrays
    • MIMO arrays
    • SAR
    • Sensor fusion
    • Optimization with handcrafted priors
    • Deep learning
  • 不足
    • Slow acquisition
    • Increased hardware complexity
    • Require large datasets
    • Limited generalizability

The proposed CoIR:

  • Key observation:

    • INR provides inductive bias towards natural solutions for imaging inverse problems
  • 方法

    • Leverages implicit neural representations (INRs) + compressed sensing
  • 贡献

    • Designed sparse linear array with 5.5x fewer antennas
    • Proposed convolutional decoder architecture ComDecoder
    • Demonstrated improved performance over standard mmWave radars and competitive untrained methods

2 RELATED WORK

2.1 mmWave Imaging Systems
  • 提高角度分辨率的方法及其缺点

    • Large physical arrays: expensive, large data volumes
    • MIMO arrays: requires many radar chips to synthesize large aperture
    • SAR techniques:slow imaging, bulky scanners
    • Sensor fusion: fails if one modality fails
    • Deep learning: requires large labeled datasets, limited generalizability
  • proposed CoIR 的不同:

    • 仅使用 single chip sparse MIMO array

    • 使用 未经训练 的 神经网络

      ✅ 无需训练数据

2.2 Sparse Radar Imaging
  • 稀疏雷达成像技术:

    • 1 Sparse aperture array designs
    • 2 Sparse reconstruction methods
  • 1 Sparse aperture array designs

    • 使用欠奈奎斯特采样 减少 天线数

    • 优化方法:

      ✅ Convex relaxations

      ✅ Prior knowledge of number of reflectors

  • 2 Sparse reconstruction methods

    • Super-resolution algorithms

      ✅ MUSIC, ESPRIT

      ✅ Require incoherent signals, known number of targets

    • Compressed sensing (CS) optimization:

      ✅ 使用稀疏先验,如 spatial sparsity, TV norm

      ✅ Challenging to design priors, scene dependent

  • proposed CoIR 的不同:

    • Sparse array design

      🚩 inspired by prior work but modified due to hardware constraints

    • Uses untrained neural network

      🚩 as complex prior instead of handcrafted prior

      ✅ Neural network prior shows affinity for natural features and noise robustness

2.3 Implicit Neural Representations
  • 两类INR architectures:

    • 1 Convolutional methods
    • 2 Coordinate-based MLP methods
  • 1 Convolutional methods ,适合:

    • Compressed sensing
    • Image super-resolution
    • Image denoising
    • Accelerated MRI
  • 2 Coordinate-based MLP methods ,适合:

    • Novel view synthesis
    • Dynamic illumination
    • PDE solutions
    • Image deconvolution
  • CoIR中的ComDecoder:

    • 属于 Convolutional methods

    • tailored for sparse radar imaging

    • Key properties:

      🚩 Convolutional operations capture local spatial information

      🚩 Upsampling induces notion of resolution per layer

      🚩 Residual blocks smooth optimization and propagate information between layers

      🚩 Together these inductive biases improve performance on sparse radar imaging

    • Differences from prior works:

      ✅ CoIR uses untrained INR as complex prior for sparse radar imaging

      ✅ Prior works use INR for natural images or other imaging modalities

3 RADAR IMAGING BACKGROUND

  • 发射信号模型

    • y t x ( t ) = e j 2 π ( f 0 t + 1 2 B τ t 2 ) , 0 ≤ t ≤ T y_{tx}(t) = e^{j2π(f_0t + \frac{1}{2}Bτt^2)}, 0 \leq t \leq T ytx(t)=ej2π(f0t+21Bτt2),0tT
    • f 0 f_0 f0: carrier frequency
    • B B B: chirp bandwidth
    • T T T: pulse duration
  • 场景模型 (离散反射体分布)

    • x ‾ [ n r , n θ ] ∈ C K × L \overline{x}[n_r, n_\theta] \in \mathbb{C}^{K\times L} x[nr,nθ]CK×L
    • n r n_r nr: range bin index
    • n θ n_\theta nθ: angle bin index
  • 回波信号模型

    • z [ n , m ] = ∑ n r = 0 K − 1 ∑ n θ = 0 L − 1 x ‾ [ n r , n θ ] e j 2 π ψ θ ( n θ ) m e j 2 π ψ r ( n r ) n + w [ n , m ] z[n,m] = \sum_{n_r=0}^{K-1} \sum_{n_\theta=0}^{L-1} \overline{x}[n_r, n_\theta] e^{j2π\psi_\theta(n_\theta)m} e^{j2π\psi_r(n_r)n} + w[n,m] z[n,m]=nr=0K1nθ=0L1x[nr,nθ]ej2πψθ(nθ)mej2πψr(nr)n+w[n,m]
    • ψ θ ( n θ ) = f 0 d c sin ⁡ ( b θ [ n θ ] ) \psi_\theta(n_\theta) = \frac{f_0 d}{c}\sin(b_\theta[n_\theta]) ψθ(nθ)=cf0dsin(bθ[nθ]): spatial frequency
    • ψ r ( n r ) = B N 2 b r [ n r ] c \psi_r(n_r) = \frac{B}{N}\frac{2b_r[n_r]}{c} ψr(nr)=NBc2br[nr]: normalized temporal frequency
    • w [ n , m ] w[n,m] w[n,m]: noise
  • Compact matrix form

    • z = F ( x ‾ ) + w z = F(\overline{x}) + w z=F(x)+w

    • F F F: 2D FFT

    • Goal: recover x ‾ \overline{x} x from under-sampled measurements z z z

4 PROPOSED METHOD

  • 目标 :

    • Recover scene reflectivity x ‾ \overline{x} x from under-sampled measurements z z z
  • Measurements :

    • z = M ⊙ F ( x ‾ ) + w z = M\odot F(\overline{x}) + w z=MF(x)+w

    • M M M: binary mask implementing under-sampling

    • w w w: noise

  • 困难 :

    • under-sampling causes grating lobes in sparse array PSF leading to aliasing in image
  • 解决方法

    • Optimize weights of untrained deep CNN G ( C ; p ) G(C;p) G(C;p) to solve inverse problem

      G G G: untrained CNN,

      C C C: fixed noise input,

      p p p: CNN parameters

    • Optimization objective:

      🚩 p ^ = arg min ⁡ p ∣ ∣ z − M ⊙ F ( G ( C ; p ) ) ∣ ∣ 2 + λ L ∣ ∣ G ( C ; p ) ∣ ∣ 1 \hat{p} = \argmin_p ||z - M\odot F(G(C;p))||_2 + \lambda_L||G(C;p)||_1 p^=argminp∣∣zMF(G(C;p))2+λL∣∣G(C;p)1

      🚩 λ L \lambda_L λL: sparsity regularization strength

    • Key observation:

      🚩 INR provides inductive bias towards natural solutions for imaging inverse problems

  • 优点 :

    • CNN architecture has high impedance to noise

    • Learned solution balances fitting salient features and suppressing artifacts

4.1 Sparse Aperture Design
  • 目标
    • Design a sparse MIMO virtual array that improves imaging accuracy when used with ComDecoder
  • 设计准测 (metrics)
    • PSF main lobe half-power beamwidth (HPBW)
    • Peak sidelobe level (SLL)
    • Presence of grating lobes
  • 硬件约束
    • Max aperture 86λ/2
    • Limited to 4 TX and 4 RX due to commercial single radar chip
  • 设计方法
    • Select 4-element minimum redundancy array for RX to avoid grating lobes
    • Grid search over TX positions to minimize SLL
  • 比较对象(baselines)
    • Full: Ideal full Nyquist sampled array
    • Sub-apt: Largest Nyquist sampled MIMO array given constraints
    • Sub-samp: Largest sub-Nyquist array given constraints
  • 设计结果
    • RX: [0, 1, 4, 6] λ/2
    • TX: [0, 46, 59, 79] λ/2
    • Gives 5.5x fewer antennas than conventional MIMO array

毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar,# 论文阅读,论文阅读,笔记,雷达成像,压缩感知

  • 优点 :
    • Avoids grating lobes
    • Minimizes HPBW
    • Minimizes SLL
    • Satisfies hardware constraints
4.2 Neural Network Architecture

提出 ComDecoder:convolutional decoder architecture

  • ComDecoder :

    • Maps latent variables C to image G(C;p)
    • 优化:Parameters p optimized to reconstruct image
  • 网络结构 :

    • Series of upsampling and residual convolution blocks
    • Use SiLU activation instead of ReLU
    • No upsampling in last layer, uses 1x1 conv instead
  • 超参数 :

    • 6 layers (including last layer)
    • 128 channels per layer
    • Fixed input C sampled from uniform distribution
  • 优化过程 :

    • Update network weights p using backpropagation and Adam
    • Takes <50 s per 256x256 image using 2000 iterations
  • 优点 :

    • SiLU increased expressivity over ReLU
    • Upsampling reinforces multi-resolution nature
    • Residual blocks enable information flow between layers
    • Inductive biases improve performance on sparse radar imaging

5 COMPETING UNTRAINED METHODS

7个baselines: Compared CoIR against several untrained methods

  • 1 Delay-and-Sum (DAS)

    • Standard beamforming method
  • 2 Sparse DAS

    • DAS with under-sampled measurements
  • 3 Gradient Descent with L1 Regularization (GD+L1 Reg)

    • Directly optimizes reflectivity distribution with sparsity prior
  • 4 Implicit Neural Representations:

    • 4.1 INR-ReLU

      ✅ MLP-based, uses Fourier feature encoding

    • 4.2 SIREN

      ✅ MLP-based, uses sinusoidal activation functions

  • 5 Deep Image Prior (DIP)

    • U-Net style convolutional autoencoder
  • 6 DeepDecoder

    • Decoder-only convolutional network
  • 7 ConvDecoder

    • Variant of DeepDecoder with some modifications

6 SIMULATION RESULTS

在仿真数据上评估所提出的CoIR

  • 仿真数据生成:

    • Synthesizes radar data cube from 2D reflectivity images
    • Uses LiDAR point clouds to generate realistic reflectivity distributions
  • 评估标准:

    • PSNR, SSIM, MAE between reconstruction and ground truth image
  • 实验:

    • 1 Vary SNR from 35dB to 11dB

      ✅ ComDecoder gave superior PSNR over all methods at all SNRs

      ✅ ComDecoder and DIP gave comparable SSIM

      ✅ ComDecoder and DIP gave lowest MAE
      毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar,# 论文阅读,论文阅读,笔记,雷达成像,压缩感知

    • 2 Visualize reconstructions at 19dB SNR

      ✅ ComDecoder gave most accurate recovery of extended reflectors

      ✅ Other CNN methods also improved over Sparse DAS

      ✅ SIREN struggled to distinguish clutter and true reflectors
      毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar,# 论文阅读,论文阅读,笔记,雷达成像,压缩感知

    • 3 Additional analyses:

      ✅ Compared different CNN decoder architectures

      ✅ Evaluated computational complexity (in supplementary)

  • 总结:

    • ComDecoder 在 simulated data 上 SOTA

7 EXPERIMENTAL RESULTS

在真实采集的Coloradar dataset上评估所有方法

  • Radar system:

    • 77 GHz FMCW with 1.282 GHz bandwidth

    • 86λ/2 uniform linear array

  • Metrics :

    • 与 full array DAS reconstruction 进行对比
  • Experiments :

    • 1 不同场景下的重建效果

      ✅ ComDecoder accurately recovered dominant features

      ✅ DIP also performed well but more artifacts than ComDecoder

      ✅ SIREN struggled in indoor scene due to noise
      毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar,# 论文阅读,论文阅读,笔记,雷达成像,压缩感知

    • 2 Evaluate 鲁棒性 across multiple outdoor scenes

      ✅ ComDecoder gave high fidelity reconstructions closest to DAS

      ✅ SIREN fit strong reflectors but also artifacts

      ✅ GD+L1 located dominant reflectors but artifacts remained

      ✅ DIP performed well but more artifacts than ComDecoder

毫米波雷达成像论文阅读笔记: IEEE TPAMI 2023 | CoIR: Compressive Implicit Radar,# 论文阅读,论文阅读,笔记,雷达成像,压缩感知

  • 总结:
    • ComDecoder 在 real data 上 SOTA
    • 显著好于其他untrained methods

8 DISCUSSION & LIMITATIONS

Limitations

  • 1 Assume static scene in forward model
    • Cannot handle moving objects
  • 2 Single bounce scattering model may not match real-world
  • 3 Slow optimization time (tens of seconds)
    • Explore different initialization strategies

Future work

  • 1 Demonstrated 2D range-angle slices due to linear array
    • 2D array needed for full 3D, but increases complexity
  • 2 CoIR could benefit other array-based imaging modalities:
    • SAR, ultrasound, etc.

9 CONCLUSION

Proposed CoIR

  • 1 Designed sparse linear array with 5.5x fewer antennas

  • 2 Proposed convolutional decoder architecture ComDecoder

  • 3 Demonstrated superior performance on simulated and real mmWave radar data文章来源地址https://www.toymoban.com/news/detail-656885.html

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